Redai dili (Sep 2023)

The Impact of Urban Green Space on Mental Wellbeing: Research Progress and Recommendations

  • Liu Ye,
  • He Jiarui,
  • Wang Ruoyu,
  • Li Zhigang

DOI
https://doi.org/10.13284/j.cnki.rddl.003733
Journal volume & issue
Vol. 43, no. 9
pp. 1747 – 1759

Abstract

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The provision of a high-quality ecological environment is essential for the quality of life of residents. As an important component of the urban ecological environment, the relationship between urban green spaces and public health requires further investigation. This paper provides a comprehensive review of the Chinese and international literature on how urban green spaces affect mental well-being. First, it introduces different approaches of measuring the use of and exposure to urban green spaces. The most commonly used indicators for measuring the use and exposure to urban green spaces include Surrounding Greenness, Access to Green Spaces, Green Viewing Rate and Green Space Quality and Usage Satisfaction. The main advantages of Surrounding Greenness are wide spatial coverage, long timespan, and low cost; however, the accuracy of measuring exposure is relatively low. Researchers have extensively used access to green spaces. Because the bird's-eye perspective cannot fully reflect resident perceptions of park green spaces, scholars have used the green view ratio, which has the advantages of wide coverage, low cost, easy access, and small data deviation. Greenspace quality and usage satisfaction are also important measurement indicators, and their main advantages are low operational difficulty and the ability to reflect residents' subjective evaluations more accurately. It then elucidates the "environmental stress reduction-restoration-instoration" mechanisms underlying the effect of urban green spaces on mental well-being. Specifically, urban green spaces can affect the mental health of residents by reducing the harm arising from heat and pollution, restoring capacity, and building capacity. Green spaces alleviate environmental pressure by purifying air, reducing noise, and alleviating the heat island effect, thereby promoting residents' mental well-being. People can alleviate their psychological stress and restore their ability to control attention by viewing green spaces, thereby protecting their mental health and providing a favorable and convenient venue for residents to conduct physical activities and socialize with their neighbors, which is beneficial to their mental wellbeing. Subsequently, it illustrates the moderating effect of opportunities to use urban green spaces, motivation to use urban green spaces, and ease of using urban green spaces on mental wellbeing from a "socio-ecological" perspective. Finally, it indicates that the current body of literature has several limitations and that future research agendas should be centered on research content, data, perspectives, and methods. Specifically, (1) for research content, the effect of green spaces on the mental well-being of different social and cultural groups is poorly understood. Therefore, it is necessary to strengthen the analysis of the sociocultural mechanism of the effect of urban green spaces to enrich the existing research framework. (2) Most previous studies used one method to measure the level of greenspace exposure or usage. It is advisable to use a variety of methods to measure the level of greenspace exposure or usage both subjectively and objectively. (3) From a research perspective, most previous studies have used a research paradigm based on local and static analysis, failing to solve the Uncertain Geographic Context Problem (UGCoP). Therefore, it is necessary to adopt a human-centered perspective and accurately measure the impact of green space exposure on residents' mental well-being in their residential neighborhoods, workplaces, and other activity spaces. (4) Researchers need to solve the problem of residential self-selection when investigating the effect of urban green spaces on mental well-being and explore nonlinear complex relationships using advanced methods such as machine learning.

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